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Field
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new theoretical approaches for understanding stability, generalization, and feature learning in large-scale neural networks. Further details and application instructions are available at: https
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project entitled "Energy Storage as an Enabler of Sustainability through Optimization, Machine Learning, and High-Performance Computing," supported by the Dieter Schwarz Foundation through a Courageous
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interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological
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part of a project entitled "Energy Storage as an Enabler of Sustainability through Optimization, Machine Learning, and High-Performance Computing," supported by the Dieter Schwarz Foundation through a
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(Master's or University Diploma) in a relevant discipline (Computer Science, Aerospace Engineering, Mechanical Engineering, Physics or similar). Sound knowledge of the fundamentals of Machine Learning, as
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of these approaches quickly, in a data-based and reliable manner. Your main tasks include: Method development: you compare and decide on methods of Machine Learning (ML) and Artificial Intelligence (AI), such as
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for a Professorship (W2) (5 years/tenure track) of Statistical and Machine Learning in the Life Sciences, combined with the lead of a research group Computational Statistics & Dynamical Systems for Life
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position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Job description:Staff Scientist / PostDoc – Machine Learning for Scientific Applications You
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implementation and commissioning Knowledge of machine learning or reinforcement learning applied to control systems is a strong asset Experience with accelerator technology, specifically photocathodes is an asset
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welcomed. The project sits at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development. Tasks of the PhD Student - Develop and